Automated negotiation has been gained amass of attentionmainly because of its broad application potential in many fields. This work studies a prominent class of automated negotiations – multi-lateral multi-issue negotiations under real-time constraints, where the negotiation agents are given no prior information about their opponents’ preferences over the negotiation outcome space. A novel negotiation approach is proposed that enables an agent to obtain efficient agreements in this challenging multi-lateral negotiations. The proposed approach achieves that goal by, (1) employing sparse pseudo-input Gaussian processes (SPGPs) tomodel opponents, (2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and (3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation.
CITATION STYLE
Chen, S., Hao, J., Zhou, S., & Weiss, G. (2017). Negotiating with unknown opponents toward multi-lateral agreement in real-time domains. Studies in Computational Intelligence, 674, 219–229. https://doi.org/10.1007/978-3-319-51563-2_17
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